The side effect of generating small fragments representing noisy structures surrounding larger target objects within the desired range of interest is undesirable, but often unavoidable, when segmenting three-dimensional tomographic data. For example, when using a global threshold method to segment a target object in a grayscale volume, each voxel is classified into object and background determined by whether a voxel belongs to a selected group of intensity threshold values. When noise and other objects are present within overlapping threshold values in the gray volume, both the target object as well as other noise structures is included in the result.
In bone segmentation of clinical Computed Tomography Angiography (CTA) data, contrast enhanced vessels, vessel calcifications, and potentially other non-anatomical objects such as stents may overlap in intensity distribution as well as other segmentation properties with the bones. With the assumption that such noise structures are disconnected from the bones, and that they are generally smaller than the bones, known bone segmentation methods often perform removal of small isolated fragments as an optional or required step to improve accuracy. One of these known bone removal methods employs a method known in the art as Region Growing to isolate and compute the size of each fragment. Fragments below a specified size threshold are removed. A major drawback of this brute force approach is that both regions belonging to the large and small fragments are required to be traversed and their size computed. Therefore, it is very processing intensive, time consuming and impractical for large amounts of data.
Alternatively, it is possible to reduce the Region Growing operation to only remove regions connected to a user provided seed point. In order to determine where to place each seed point for each fragment to be removed, the 3D volume data is first projected onto a 2D image using a method known in the art as Volume Rendering, and subsequently, a user visually identifies the fragments in the 2D image and manually places 2D points over the fragments to be removed. The 2D points are then transformed to corresponding 3D coordinates. These coordinates are then used as seed points for 3D Region Growing to determine the boundaries of the identified segments and remove them from the volume data. Manual picking and removal of such fragments is only feasible if the number of fragments is not many, otherwise, this becomes a tedious and time consuming approach. Unfortunately, large tomographic medical data tend to have a large number of such undesired fragments and require longer processing time.
In particular, Computed Tomography (CT) data generated by modern multi-slice CT scanners are capable of generating studies with more than one thousand slices. CTA studies contain as many as two thousand slices. A large 3D volume of size 512×512×1000, where 512×512 is the slice resolution and 1000 is the number of slices, contains over 262 million volume elements (also known as voxels). It is therefore neither practical to remove noise fragments by brute force automatic methods, nor by picking each fragment manually. There is a need for a method for automatically removing small fragments in segmented 3D volumes that is accurate and efficient.